Abstract
Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large individual differences, EEG feature extraction and classification have the problems of low accuracy and efficiency. To solve this problem, this paper proposes a recognition method of motor imagery EEG signal based on deep convolution network. This method firstly aims at the problem of low quality of EEG signal characteristic data, and uses short-time Fourier transform (STFT) and continuous Morlet wavelet transform (CMWT) to preprocess the collected experimental data sets based on time series characteristics. So as to obtain EEG signals that are distinct and have time-frequency characteristics. And based on the improved CNN network model to efficiently recognize EEG signals, to achieve high-quality EEG feature extraction and classification. Further improve the quality of EEG signal feature acquisition, and ensure the high accuracy and precision of EEG signal recognition. Finally, the proposed method is validated based on the BCI competiton dataset and laboratory measured data. Experimental results show that the accuracy of this method for EEG signal recognition is 0.9324, the precision is 0.9653, and the AUC is 0.9464. It shows good practicality and applicability.
Highlights
Brain-Computer Interface (BCI) is a communication control system established between the brain and external devices through signals generated during brain activity (Gao, 2008; Mohamed et al, 2017; Wang et al, 2019)
Drawing on the existing research work of EEG signal recognition, this paper proposes a motor imagery EEG signal recognition based on deep convolutional network
Performance Comparison With Several Comparison Algorithms on Public Datasets In order to verify the superiority of the deep convolutional network proposed in this paper in EEG signal recognition, this paper reproduced three other analysis methods based on the mixed motor imagery data set [Literature (Lawhern et al, 2018; Wei et al, 2019; Li, 2020)]
Summary
Brain-Computer Interface (BCI) is a communication control system established between the brain and external devices (computers or other electronic devices) through signals generated during brain activity (Gao, 2008; Mohamed et al, 2017; Wang et al, 2019). Among the many deep learning algorithms, Convolutional Neural Network (CNN) has become the most popular method in the motor imagination EEG classification algorithm because of its excellent feature extraction capabilities. Based on the advantages of the convolutional neural network’s own network model, combined with the CSP algorithm, twolevel feature extraction and classification are performed on the motor imagination EEG signal. The EEG signal classification accuracy rate of the measured data set increases first and decreases as the size of the convolution kernel increases, but the optimal convolution kernel size is 1 × 55 This shows that for different experimental subjects, convolution kernels of different sizes are needed to extract the FIGURE 8 | Classification results analysis based on temporal sequence
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